Certification in
Artificial Intelligence and
Machine Learning
Certification in
Artificial Intelligence and
Machine Learning
- Learn from IIT faculty and industry mentors
- 12+ industry tools like Python, Numpy, Pandas, Jupyter
- Multiple Hands-On Al projects, 1 Capstone Project
Foundation Starts
February 04, 2026
Course Duration
9 Months
Time Commitment
8-10 Hours/week
Eligibility
12th Pass and Above
Learning Mode
Online
Foundation Starts
February 04, 2026
Course Duration
9 Months
Time Commitment
8-10 Hours/week
Eligibility
12th Pass and Above
Learning Mode
Online
2.4K+ students have already registered
What Will You Learn?
Begin your AI/ML journey with a beginner-friendly, industry-aligned curriculum from Vishlesan i-Hub, IIT Patna. Covering everything from Python basics to advanced topics like deep learning and generative AI, the program builds real-world skills through hands-on projects and expert-led learning.
Toolkit
Course Details
Duration
9 Months
Course Mode
Online
Certification
From Vishlesan i-Hub IIT Patna
Module-1: AIM101 – Introduction to AI and ML for Beginners
- Fundamentals of Computing and Python Basics : Introduction to programming concepts, setting up a development environment (e.g., Jupyter Notebooks).
- Essential Mathematics for AI/ML : Basic linear algebra, calculus, probability, and statistics.
- Data Representation and Visualization : Using Pandas for data manipulation and Matplotlib for data visualization.
- Core AI Concepts and History : Defining AI, early developments, key breakthroughs in AI and ML.
- Introduction to Machine Learning : Understanding AI vs. ML, supervised vs. unsupervised learning, basic Python examples.
- Data Collection and Cleaning : Techniques for gathering and cleaning data for analysis.
Module-2: AIM201 – Core Machine Learning Techniques and Practices
- Exploratory Data Analysis (EDA) : Summarizing data, identifying patterns, and performing basic statistical tests.
- Basic Regression and Classification : Understanding and implementing linear regression and nearest neighbor classification.
- Evaluation Metrics and Model Validation : Applying train/test split, accuracy, RMSE, and cross-validation.
- Intro to Ethical AI : Addressing bias in data and responsible AI usage.
- Case Studies : Real-world applications of AI in healthcare, finance, and education.
- Recap of Machine Learning Workflow : Data preprocessing, feature engineering, model training, and evaluation.
- Regression Techniques : Multiple linear regression, polynomial regression, and regularization.
- Classification Algorithms : Logistic regression, decision trees, and ensemble methods.
- Advanced Unsupervised Learning : Clustering, dimensionality reduction, and anomaly detection.
- Neural Network Fundamentals : Perceptrons, activation functions, feed-forward networks, and backpropagation.
- Model Evaluation and Metrics : Precision, recall, F1-score, confusion matrix, and AUC-ROC.
- Techniques for Overcoming Overfitting : Regularization, dropout, data augmentation, and early stopping.
- Hyperparameter Tuning : Grid search, random search, and Bayesian optimization.
- Practical ML Pipeline Development : Building end-to-end ML pipelines and MLOps basics.
Module-3: AIM301 – Advanced AI: Deep Learning, NLP, and Emerging Trends
- Deep Learning Architectures : Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and sequence models.
- Advanced Neural Network Topics : Batch normalization, residual networks, and transfer learning techniques.
- Natural Language Processing (NLP) : Text preprocessing, word embeddings, and sequence-to-sequence models.
- Transformers and Language Models : BERT, GPT, attention mechanisms, and fine-tuning for tasks like text classification and summarization.
- Computer Vision Beyond Basics : Object detection, image segmentation, and pretrained models.
- Reinforcement Learning Foundations : Markov Decision Processes (MDPs), Q-learning, and policy gradients.
- Generative Models : GANs, autoencoders, and their applications in AI.
- Large-Scale Data Processing and Deployment : Distributed computing, cloud-based AI platforms, and Docker containerization.
- AutoML and Advanced Hyperparameter Tuning : Automated feature engineering and advanced search strategies.
- Frontiers of AI and Research Directions : Ethical AI, fairness in large models, AI governance, and current AI research trends.
- Capstone Project : Integration of multiple advanced AI techniques to develop a robust AI solution.
Please Note :- "Due to the evolving nature of the industry expectations and partner institute feedback, some syllabus aspects may change. Any updates will be communicated during the Inauguration Session(s) or at the start of the relevant module"
Our Instructors & Industry Mentors
Fee Structure
Qualifier Test Fee
(non-refundable)
₹99
Option 1
Upfront
Option 2
EMI
(Through Our NBFC Partners)
Secure Seat Fee
(non-refundable)
₹4,000
₹4,000
Program Fee
(non-refundable)
₹51,085
₹6,528 x 9 months
Total
₹55,085
₹62,752
